Author:
DANILCZUK Wojciech,GOLA Arkadiusz
Abstract
Effective decision-making in industry conditions requires access and proper presentation of manufacturing data on the realised manufacturing process. Although the frequently applied ERP systems allow for recording economic events, their potential for decision support is limited. The article presents an original system for reporting manufacturing data based on Business Intelligence technology as a support for junior and middle management. As an example a possibility of utilising data from ERP systems to support decision-making in the field of purchases and logistics in small and medium enterprises.
Subject
Artificial Intelligence,Industrial and Manufacturing Engineering,Computer Science Applications,Economics, Econometrics and Finance (miscellaneous),Mechanical Engineering,Biomedical Engineering,Information Systems,Control and Systems Engineering
Reference26 articles.
1. Alsoub, R.K., Alrawashdeh, T.A., & Althunibat, A. (2018). User acceptance for Enterprise Resource Planning Software Systems. International Journal of Innovative Computing Information and Control, 14(1), 297–307. http://doi.org/10.24507/ijicic.14.01.297
2. Aremu, A.Y., Shahzad, A., & Hassan, S. (2019). The Empirical Evidence of Enterprise Resource Planning System Adoption and Implementation on Firm’s Performance Among Medium-sized Enterprises. Global Business Review, UNSP 0972150919849751. http://doi.org/10.1177/0972150919849751
3. Bocewicz, G., Nielsen, I., & Banaszak, Z. (2016). Production Flows Scheduling Subject to Fuzzy Processing Time Constraints. International Journal of Computer Integrated Manufacturing, 29(10), 1105–1127. http://doi.org/10.1080/0951192X.2016.1145739
4. Chang, Y.W. (2020). What drives organizations to switch to cloud ERP systems? The impacts of enablers and inhibitors. Journal of Enterprise Information Management, 33(3), 600–626. http://doi.org/10.1108/JEIM-06-2019-0148
5. Cieśla, B., & Gunia, G. (2019). Development of integrated management information systems in the context of Industry 4.0. Applied Computer Science, 15(4), 37–48. http://doi.org/10.23743/acs-2019-28
Cited by
4 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献